What challenges did AZ Delta face before AI implementation?

AZ Delta needed to analyze a vast amount of complex and diverse patient data from electronic medical records to generate insights for personalized medicine. Their on-premises IT infrastructure lacked the power to handle large-scale data analytics quickly and securely. Medical data complexity required data normalization and secure handling to protect sensitive patient information.

What was the impact of the AI solution?

Data query response times were cut from 15 minutes to 15 seconds, greatly improving efficiency. Physicians can now access rapid insights to better plan personalized treatments. The platform securely handles hundreds of millions of medical data points, enhancing data-driven clinical decision-making. Plans to incorporate additional data types like ECG and pathology images will further improve personalized medicine efforts.

Source

GCPEvidence: Medium50/100

AZ Delta Hospital Enhances Personalized Medicine and Data Analytics Using Google Cloud BigQuery and AI

Use case typePrecision medicineUpdated Jun 13, 2026

AZ Delta, one of Belgium's largest hospitals, faced challenges in analyzing complex and diverse patient medical records to enable personalized treatment while ensuring data security. The hospital's on-premises infrastructure was inadequate for large-scale medical data analytics, causing slow query times and difficulty handling vast, varied data. AZ Delta migrated its medical data to Google Cloud, utilizing BigQuery for fast querying, Google Kubernetes Engine for ML model deployment, Virtual Private Cloud for security, Cloud Identity for authentication, and Apache Airflow for ETL processes. AZ Delta collaborated with ML6 for AI expertise and security to build a scalable, secure medical data analytics platform. The solution reduced data query times from 15 minutes to 15 seconds, enabling rapid insights for clinical decision-making and personalized treatment planning, with plans to expand the platform with additional data types like electrocardiograms and pathology images.

Organization: AZ Delta
Industry: Healthcare
Location: Belgium

Reported outcomes

15 minutes

timeTime & speed

15 secondstime

Strategic outcomes

New product / capabilityEnabled personalized treatment insightsBetter decisions & insightImproved clinical decision-makingSpeed & agilityAccelerated medical data queryingNew product / capabilityExpanded platform to new data types

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Time: 15 minutes decrease

Google Cloud Customer StoriesCustomer storyInferred claimMedium evidence strength

Data query response times were cut from 15 minutes to 15 seconds, greatly improving efficiency.

Normalized claim

Time: 15 seconds decrease

Google Cloud Customer StoriesCustomer storyInferred claimMedium evidence strength

Data query response times were cut from 15 minutes to 15 seconds, greatly improving efficiency.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: AZ Delta
Provider: GCP
Maturity: Unknown
Linked source: Google Cloud Customer Stories

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 2 of 2

1Data Analytics
2Personalized Medicine

AZ Delta needed to analyze a vast amount of complex and diverse patient data from electronic medical records to generate insights for personalized medicine.
Their on-premises IT infrastructure lacked the power to handle large-scale data analytics quickly and securely.
Medical data complexity required data normalization and secure handling to protect sensitive patient information.

Migrated medical data to Google Cloud using a secure Virtual Private Cloud and multi-factor authentication with Cloud Identity to protect sensitive data.
Used BigQuery to enable fast, large-scale querying and analysis, reducing query times significantly.
Employed Apache Airflow for efficient ETL operations and Google Kubernetes Engine for hosting and deploying machine learning models.
Partnered with ML6 for building the platform and ensuring comprehensive security compliance.

Technologies

BigQuery Google Kubernetes Engine Virtual Private Cloud Cloud Identity Apache Airflow

Data query response times were cut from 15 minutes to 15 seconds, greatly improving efficiency.
Physicians can now access rapid insights to better plan personalized treatments.
The platform securely handles hundreds of millions of medical data points, enhancing data-driven clinical decision-making.
Plans to incorporate additional data types like ECG and pathology images will further improve personalized medicine efforts.

Architecture

AZ Delta implemented a comprehensive analytics platform using Google Cloud services including BigQuery, Virtual Private Cloud, Cloud Identity for three-factor authentication, Apache Airflow for ETL operations, and Google Kubernetes Engine for machine learning model deployment.

Implementation partners1

ML6

Sources & evidence1

Evidence: Medium50/100Evidence strength